



from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
 from fastapi import Body, FastAPI, HTTPException
 from fastapi.routing import APIRoute
 from pydantic import BaseModel
 from starlette.responses import JSONResponse
 from uvicorn import run

 a_  :Tuple     = True
except (ImportError, AttributeError):
 a_  :Any     = object
 def 			lowercase_				(*A					:					List[Any]						,   **A					:					Union[str, Any] ):
        pass





 a_  :Optional[int]     = False


a_  :Any     = logging.get_logger("transformers-cli/serving")
def 			lowercase_				(A					:					List[Any] ):
       snake_case__				:      int        =      pipeline(
           task=args.task						,   model=args.model if args.model else None						,   config=args.config						,   tokenizer=args.tokenizer						,   device=args.device						,   )
       return ServeCommand(__lowerCAmelCase						,   args.host						,   args.port						,   args.workers )




class    snake_case__		(			__lowerCAmelCase	):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE          =      42




class    snake_case__		(			__lowerCAmelCase	):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE          =      42
     _SCREAMING_SNAKE_CASE          =      42




class    snake_case__		(			__lowerCAmelCase	):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE          =      42




class    snake_case__		(			__lowerCAmelCase	):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE          =      42




class    snake_case__		(			__lowerCAmelCase	):






     """simple docstring"""


     @staticmethod
     def   lowercase_  (			_snake_case    :    Optional[int]   )  ->Optional[Any]:
            snake_case__				:      int        =      parser.add_parser(
                'serve',					help='CLI tool to run inference requests through REST and GraphQL endpoints.'   )
            serve_parser.add_argument(
                '--task',					type=_lowerCamelCase,					choices=get_supported_tasks(),					help='The task to run the pipeline on',					)
            serve_parser.add_argument('--host',					type=_lowerCamelCase,					default='localhost',					help='Interface the server will listen on.'   )
            serve_parser.add_argument('--port',					type=_lowerCamelCase,					default=8_8_8_8,					help='Port the serving will listen to.'   )
            serve_parser.add_argument('--workers',					type=_lowerCamelCase,					default=1,					help='Number of http workers'   )
            serve_parser.add_argument('--model',					type=_lowerCamelCase,					help='Model\'s name or path to stored model.'   )
            serve_parser.add_argument('--config',					type=_lowerCamelCase,					help='Model\'s config name or path to stored model.'   )
            serve_parser.add_argument('--tokenizer',					type=_lowerCamelCase,					help='Tokenizer name to use.'   )
            serve_parser.add_argument(
                '--device',					type=_lowerCamelCase,					default=-1,					help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)',					)
            serve_parser.set_defaults(func=_lowerCamelCase   )






     def __init__(			self    :    Optional[int],					_snake_case    :    Optional[Any],					_snake_case    :    Optional[int],					_snake_case    :    int,					_snake_case    :    int   )  ->str:
            snake_case__				:      Union[str, Any]        =      pipeline

            snake_case__				:      Optional[Any]        =      host
            snake_case__				:      Union[str, Any]        =      port
            snake_case__				:      Any        =      workers

            if not _serve_dependencies_installed:
                   raise RuntimeError(
                       'Using serve command requires FastAPI and uvicorn. '
                       'Please install transformers with [serving]: pip install \"transformers[serving]\".'
                       'Or install FastAPI and uvicorn separately.'   )
            else:
                   logger.info(F'''Serving model over {host}:{port}'''   )
                   snake_case__				:      List[str]        =      FastAPI(
                       routes=[
                           APIRoute(
                               '/',					self.model_info,					response_model=_lowerCamelCase,					response_class=_lowerCamelCase,					methods=['GET'],					),
                           APIRoute(
                               '/tokenize',					self.tokenize,					response_model=_lowerCamelCase,					response_class=_lowerCamelCase,					methods=['POST'],					),
                           APIRoute(
                               '/detokenize',					self.detokenize,					response_model=_lowerCamelCase,					response_class=_lowerCamelCase,					methods=['POST'],					),
                           APIRoute(
                               '/forward',					self.forward,					response_model=_lowerCamelCase,					response_class=_lowerCamelCase,					methods=['POST'],					),
                       ],					timeout=6_0_0,					)






     def   lowercase_  (			self    :    Any   )  ->Dict:
            run(self._app,					host=self.host,					port=self.port,					workers=self.workers   )






     def   lowercase_  (			self    :    List[str]   )  ->Optional[Any]:
            return ServeModelInfoResult(infos=vars(self._pipeline.model.config   )   )






     def   lowercase_  (			self    :    Dict,					_snake_case    :    int = Body(_lowerCamelCase,					embed=_lowerCamelCase   ),					_snake_case    :    int = Body(_lowerCamelCase,					embed=_lowerCamelCase   )   )  ->List[str]:
            try:
                   snake_case__				:      Union[str, Any]        =      self._pipeline.tokenizer.tokenize(_lowerCamelCase   )

                   if return_ids:
                          snake_case__				:      Tuple        =      self._pipeline.tokenizer.convert_tokens_to_ids(_lowerCamelCase   )
                          return ServeTokenizeResult(tokens=_lowerCamelCase,					tokens_ids=_lowerCamelCase   )
                   else:
                          return ServeTokenizeResult(tokens=_lowerCamelCase   )

            except Exception as e:
                   raise HTTPException(status_code=5_0_0,					detail={'model': '', 'error': str(_lowerCamelCase   )}   )






     def   lowercase_  (			self    :    Union[str, Any],					_snake_case    :    Optional[int] = Body(_lowerCamelCase,					embed=_lowerCamelCase   ),					_snake_case    :    Union[str, Any] = Body(_lowerCamelCase,					embed=_lowerCamelCase   ),					_snake_case    :    Union[str, Any] = Body(_lowerCamelCase,					embed=_lowerCamelCase   ),					)  ->Optional[int]:
            try:
                   snake_case__				:      List[Any]        =      self._pipeline.tokenizer.decode(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   )
                   return ServeDeTokenizeResult(model='',					text=_lowerCamelCase   )
            except Exception as e:
                   raise HTTPException(status_code=5_0_0,					detail={'model': '', 'error': str(_lowerCamelCase   )}   )






     async def   lowercase_  (			self    :    str,					_snake_case    :    Optional[Any]=Body(_lowerCamelCase,					embed=_lowerCamelCase   )   )  ->List[str]:

            # Check we don't have empty string
            if len(_lowerCamelCase   ) == 0:
                   return ServeForwardResult(output=[],					attention=[]   )

            try:
                   # Forward through the model
                   snake_case__				:      Union[str, Any]        =      self._pipeline(_lowerCamelCase   )
                   return ServeForwardResult(output=_lowerCamelCase   )
            except Exception as e:
                   raise HTTPException(5_0_0,					{'error': str(_lowerCamelCase   )}   )







"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


